Location: Meat Safety and Quality
Project Number: 3040-42000-020-000-D
Project Type: In-House Appropriated
Start Date: Jan 19, 2021
End Date: Jan 18, 2026
Objective:
Objective 1: Evaluate longitudinal ecology of foodborne pathogens in food-animal production continuum.
Sub-objective 1.A: Determine the population diversity of Shiga toxigenic Escherichia coli in a closed cattle feedlot.
Sub-objective 1.B: Determine the population dynamics of Salmonella at cattle feeding operations.
Objective 2: Application of bioinformatic tools to identify factors that contribute to virulence and persistence in foodborne pathogens.
Sub-objective 2.A: Development of machine learning approaches for predicting Shiga toxigenic E. coli and Salmonella pathogenicity in humans.
Sub-objective 2.B: In vitro pathogenicity assays and transcriptomic analyses to examine putative virulence factor contribution to Salmonella enterica pathogenicity, in order to increase our understanding of the strains encountered in production agriculture that have the greatest potential impact on human health.
Sub-objective 2.C: Characterization of environmental impacts on pathogen resistance to antimicrobials and sanitizers.
Objective 3: Development and validation of tools that enable regulators, food-animal producers and processors to monitor high-risk foodborne pathogens.
Approach:
Foodborne illness and the resulting loss of productivity in the United States are reportedly greater than $14 billion a year. While research efforts have resulted in significant strides in tracking contamination entry points and identifying mitigation strategies, outlier events continue to occur, and complete prevention of foodborne pathogens entering the food chain remains an elusive goal. Moreover, concerns persist among regulators and health care advocates that antimicrobial use during animal production may impact antimicrobial resistance levels and potential for transfer to foodborne pathogens. Accordingly, the research described here aims to provide new information about these issues by 1) increasing our understanding of both the genomic diversity and persistence of pathogens over space and time in agricultural settings, which will improve foodborne illness traceback investigations 2) improve understanding of the movement of antimicrobial resistance genes among natural reservoirs and foodborne pathogens; 3) using machine learning to identify predictive markers that can be used to rapidly screen samples or isolates for important phenotypic characteristics and further phenotypically characterizing strains predicted to be more pathogenic or persistent in production settings; and 4) developing tools to monitor high-risk foodborne pathogens, including methods for rapidly estimating levels of foodborne pathogens in meat products. The information generated by this research will facilitate the development of solutions to decrease the incidence of pathogen exposure from the meat food chain. The results of this research will be of interest to food regulatory agencies, the pathogen testing industry, livestock and meat processing industries, agricultural and biomedical scientists, and public health professionals. Major beneficiaries of the successful realization and manifestation of the research goals would ultimately be consumers of a safer food supply.
